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AtmoSTEM: From high-resolution emission mapping to environmental intelligence
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Webinar with Anastasia Kakouri, University of Aegean. The spatial and temporal representation of anthropogenic emissions is essential for atmospheric modelling, yet its influence on the predictive performance of data-driven air quality models remains insufficiently assessed. This study introduces AtmoSTEM, a modular European framework integrating high-resolution emission modelling with machine learning-based concentration prediction. AtmoSTEM combines CAMS-REG emission inventories, sector-specific geospatial proxies, and CAMS-TEMPO profiles to generate spatially and temporally resolved anthropogenic emissions. The initial application covers daily PM2.5 and PM10 emissions at 1 km spatial resolution across Europe for 2015–2024. The resulting emissions are integrated with meteorological, satellite-derived, atmospheric composition, and geospatial information to support machine learning-based predictions of ground-level pollutant concentrations. An Extreme Gradient Boosting (XGBoost) approach is employed to investigate the relationships between anthropogenic emissions, environmental conditions, and observed air pollution levels, with particular emphasis on the contribution of high-resolution emission information to predictive performance. Model predictions are evaluated against independent ground-based observations from air quality monitoring stations across Europe. The study aims to assess the potential of integrating high-resolution spatiotemporal emissions with machine learning for air quality prediction, providing a basis for more spatially detailed population exposure assessments and epidemiological research.
2027 Nordic Workshop on AI for Climate
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The 2027 Nordic Workshop on AI for Climate will gather researchers from the Nordics. This one-day, in-person workshop, will take place in Oslo, April 22n 2027. The workshop will feature a mix of keynotes, oral presentations, and posters around the topics of AI for tackling climate change, including AI for biodiversity and the green transition. The workshop will be a meeting point for a wide range of researchers from (primarily) around the Nordic countries.
Climate AI Nordics Newsletter, September 2026
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The September edition of the Climate AI Nordics newsletter highlights key community updates, including the Wikimpacts extreme-event database, member feature Stefanos Georganos, and recaps of climate-focused workshops from ECCV 2026. It also details upcoming webinars on satellite perception, the announcement of the 2027 Nordic Workshop in Oslo, and multiple academic and industry job openings across the Nordic region.
Featured project: Wikimpacts - Automated global climate impact database
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Wikimpacts 1.0 is an open-access global database of extreme climate event impacts built by extracting structured data from Wikipedia using large language models and natural language processing. Developed with contributions from RISE researchers as part of the CLIMES Center of Excellence (a partner organisation to Climate AI Nordics), Wikimpacts covers over 2,700 historical events and introduces granular sub-national impact data to empower climate resilience research.